Examinando por Autor "Ruiz-Molina, Juan Carlos"
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Ítem Multisensor Fusion Estimation for Systems with Uncertain Measurements, Based on Reduced Dimension Hypercomplex Techniques(MDPI, 2022-07-18) Fernández-Alcalá, Rosa María; Jiménez-López, José Domingo; Navarro-Moreno, Jesús; Ruiz-Molina, Juan CarlosThe prediction and smoothing fusion problems in multisensor systems with mixed uncertainties and correlated noises are addressed in the tessarine domain, under Tk-properness conditions. Bernoulli distributed random tessarine processes are introduced to describe one-step randomly delayed and missing measurements. Centralized and distributed fusion methods are applied in a Tk-proper setting, k = 1, 2, which considerably reduce the dimension of the processes involved. As a consequence, efficient centralized and distributed fusion prediction and smoothing algorithms are devised with a lower computational cost than that derived from a real formalism. The performance of these algorithms is analyzed by using numerical simulations where different uncertainty situations are considered: updated/delayed and missing measurements.Ítem Tessarine signal processing under the T -properness condition(Elsevier, 2020-09) Navarro-Moreno, Jesús; Fernández-Alcalá, Rosa María; Jiménez-López, José Domingo; Ruiz-Molina, Juan CarlosThe paper analyzes the processing of 4D commutative hypercomplex or tessarine signals under properness conditions. Firstly, the concept of T-properness is introduced and a procedure to test experimentally whether a tessarine random signal is proper or not is proposed. Then, for the class of T-proper signals, the linear minimum mean square error estimation problem is addressed. In this regard, it should be highlighted that although the tessarine algebra is not a Hilbert space, a metric which guarantees the existence and unicity of the optimal estimator is defined. Moreover, the equivalence, under T-properness conditions, between the optimal estimator based on a tessarine widely linear processing and the one based on a tessarine strictly linear (TSL) processing is also shown, attaining thus a notable reduction in computational burden. Finally, two T-proper models, a TSL state-space model, and a TSL stationary model, from which the optimal estimator can be recursively obtained are considered. In both cases, simulated examples are developed where the superiority of TSL processing over the counterparts in the quaternion domain is exhibited.